IBM Deploys AI to Induce Positive Disruptions

The colossal field of artificial intelligence has witnessed exponential growth in recent years. With the advent of deep neural networks (DNNs) that surpass human cognition and human intelligence, the post-modern world is now thriving on disruptive technologies and avant-garde innovations. Adhering to the ebb and flow of innovations and technological transformations, IBM is constantly experimenting with AI to explore the terra incognita and attach new dimensions to disruptive technology.

In recent days, IBM is striving to combine AI and hardware devices. Such a move will not only catalyze digital transformation in business industries and IT hubs but will also emerge as another disruptive technology at large.

The Exceptional Conflation of AI and Hardware: Aims and Objectives

Pushing the limits of AI, IBM is currently on a mission to instill AI in hardware. The IBM Research AI Hardware center is the hub of a team of academicians and industry leaders who are experimenting to give birth to the next wave of AI technologies. The mission of such a development is that it will deliver 2.5 times annual improvement in hardware that is capable of high compute efficiency. This development in hardware is deemed as one of the key components of IBM’s “Fluid Intelligence”.

With this development, IBM is looking forward to a 16-fold improvement in processing efficiency. IBM is delving into a world of analog computation, which can generate high performance in low power.

Additionally, IBM has also announced its third-generation digital AI core. IBM asserts that this will develop a new four-core design that accelerates the performance efficiency of training. This will significantly surpass and outpace the goal of 2.5X global improvements.

Another meticulous AI in hardware development is analog computing. IBM has devoted itself to experimenting with analog computers for decades. It has recently developed a chip using phase-memory (PCM) in order to encode the neural nets on a memory device.

It is said, “Time is money.” Business industries practice timeliness vehemently. This is taken as the primary reason to adopt high-end technologies to cope up with the cut-throat race in the market space.

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Despite their nature, the vulnerabilities didn’t pose a serious threat

The Python programming language has been forced to scurry to put out a new release of the popular programming language to address several security issues.

While the fix for the security flaws had already been pushed in the release candidate, the Python community urged the developers to bring them over to a stable release as soon as possible.

“Since the announcement of the release candidates for 3.9.2 on 3.8.8, we received a number of inquiries from end users urging us to expedite the final releases due to the security content, especially CVE-2021-3177,” note Python’s release team members in the release notes.

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Toothless exploits

The vulnerability tracked as CVE-2021-3177 that spooked the community in particular is a remote code execution (RCE) flaw that could theoretically allow threat actors to execute arbitrary commands or code on a target machine. Moreover, it’s existed in all Python 3 releases through to Python 3.9.1.

However, the release team notes that practical exploits of the vulnerability were “very unlikely” since several conditions had to be met for a successful exploit. They go on to refer to Red Hat’s analysis of the vulnerability who note that “the highest threat from this vulnerability is to system availability.” 

Be that as it may, the issue has been fixed and the release team urges all Python users to switch to the latest release. Reports also note that long term support (LTS) releases such as Debian are backporting the security patches to ensure that earlier Python versions are inoculated as well.

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Via: ZDNet

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Facebook seeks to placate lawmakers over Libra but Mnuchin raises “serious concerns”
Facebook has made known that it won’t launch its digital currency Libra until it receives regulatory approval especially after US Treasury Secretary Steve Mnuchin expresses very serious concerns that Libra could be misused by terrorists. Apart from being supervised by the Swiss Financial Markets Supervisory Authority it will also register in the US with FinCEN. Libra does not intend to compete with any sovereign currencies or intruding the monetary policy arena. It is designed to act like cash only to create economic empowerment.

Google’s new ‘data echoing’ technique speeds up AI training
Search engine giant, Google’s researchers think that the best-performing data echoing algorithms can match the baseline’s predictive performance using less upstream processing. The approach used by these researchers in data echoing inserts a stage in the pipeline that repeats the output data of the previous stage, recovering idle compute capacity. Data echoing is an alternative to optimizing the training pipeline or adding additional workers to perform upstream data processing which may not be possible.

Salesforce named a leader in data management platforms by independent research firm
CRM solutions provider, Salesforce has been recognised a leader by Forrester Research in its report namely ‘The Forrester Wave: Data Management Platforms,’ Q2 2019. Salesforce’s Data Management Platform (DMP), Audience Studio enables marketers to know, personalise and engage their customers across all verticals. It also provides a platform to capture customer data, segment audiences with Einstein AI for engagement. Salesforce Marketing Cloud helps marketers customise marketing with Einstein and analyse the impact to improve their campaign performance.

Synthio introduces Contact Data Maintenance for Salesforce
Synthio has revealed immediate availability of its Contact Data Management for Salesforce (CDM-SF) platform. This platform compares a customer’s Salesforce database against more than 160 million data records and automatically updates it with complete and correct information. Companies implementing CDM-SF are certain about the data they receive being correct and complete in order to build Account Based Marketing and demand generation programs, customer billing, and partner management.

IBM builds AI ladder with partnerships
Multinational information technology company, IBM has joined forces with data preparation technology provider Trifacta to improve the data preparation technology it provides in its data and artificial intelligence stack. This partnership has resulted in InfoSphere Advanced Data Preparation to enable organisations in formatting, structuring and enriching datasets while working with their existing environments including data lakes. Trifacta’s clients include GSK, the Centers for Disease Control, Google, and Pepsico.

IBM announces AutoAI For Watson Studio
IBM has announced AutoAI, a new set of capabilities for Watson Studio to automate complicated tasks associated with designing, optimising and governing AI in the enterprise. Watson Studio’s AI capabilities work efficiently with Watson Machine Learning (ML) to solve the challenges by automating and speeding steps in the AI lifecycle. The AutoAI capabilities are available on the IBM Cloud and they automate data prep and pre-processing including model development and feature engineering. They also contain a suite of powerful model types for enterprise data science and aim to scale ML deployment quickly.
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IBM offers employees ‘master’s programs’ in AI and data science
IBM is now offering its employees the opportunities to take courses in data science and artificial intelligence with Simplilearn. The courses will be web-based including live virtual classrooms access to teaching assistants, self-paced video instruction courses and assessments. Upon completion, employees will be granted certificates from Simplilearn and IBM.

Oracle bets big on ‘autonomous offering’ for security as cyber criminals embrace AI, ML
Cloud services provider, Oracle aims to automate its database by creating a tool, Web Application Firewall, that eliminates manual errors in database tuning, security, backups, and updates. The cloud service provider believes that security should be embedded by default. This tool makes use of AI and ML to secure itself and has features that can identify good bots from bad bots by furthering whitelisting good bots letting more traffic in and blacklisting bad bots.

Amazon partners with the NHS to help UK patients access health advice
Amazon has partnered with UK’s NHS to help elderly people access their health advice online through Amazon’s AI-powered voice assistant Alexa. The Department of Health is looking forward to this partnership to empower patients take control of their health. The UK government has set up a new unit called NHSX to boost the use of digital technologies in the UK health service. Secretary of State for Health and Social Care, Matt Hancock, said: “We want to empower every patient to take better control of their healthcare and technology like this is a great example of how people can access reliable, world-leading NHS advice from the comfort of their home, reducing the pressure on our hardworking GPs and pharmacists.”

FCA to explore the ‘explainability’ of AI in consumer finance
The Financial Conduct Authority (FCA) is joining forces with the Alan Turing Institute to test the transparency and explainability of AI in the financial sector. The watchdog wants a practical understanding of machine learning and its functions in comparison to just debates. Christopher Woolard, executive director of strategy and competition at the FCA said: “AI and its application in financial services is causing us to ask big questions – and the answers we arrive at have the potential to fundamentally alter society and the established order.”

Samsung Pay integrates loan and credit card applications in India
Samsung Pay has tapped Paisabazaar to let people apply for things such as credit cards and loans through Samsung Pay. This service calls Paisabazaar’s ‘Chance of Approval’ feature that makes use of predictive algorithms to connect consumers with suitable lenders with a possibility of approving a loan. Sanjay Razdan, senior director, services, management, Samsung India, says: “Our partnership with Paisabazaar.com has helped us create a holistic platform that gives them the comfort of opting for various financial products besides using it as a payment platform.”

Lloyds partners with Blue Motor Finance to develop instant payments API
Lloyds Bank has partnered with Blue Motor Finance to provide motor dealerships with realtime loan payments through a commercial direct debit API collaboration. Co-developed by Lloyds Bank Commercial Banking API Lab, the programme taps the Faster Payment rails and aims to deliver instant loans to motor trader clients of Blue. Steve Everett, MD, payments and cash Management, Lloyds Bank Commercial Banking said: “We’re excited to partner with Blue Motor Finance to bring this innovative instant payment service to its motor trader customers and create real value for businesses in the car financing industry through new API-enabled solutions.”

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Applying big data analysis to mineralogy offers a way to predict minerals missing from those known to science, where to find them, and where to find new deposits of valuable minerals such as gold and copper, according to a groundbreaking study.

In a paper published by American Mineralogist, scientists report the first application to mineralogy of network theory (best known for analysis of e.g. the spread of disease, terrorist networks, or Facebook connections).

The results, they say, pioneer a way to reveal mineral diversity and distribution worldwide, mineral evolution through deep time, new trends, and new deposits.

Led by Shaunna Morrison of the Deep Carbon Observatory and DCO Executive Director Robert Hazen (both at the Carnegie Institution for Science in Washington, D.C.), the paper’s 12 authors include DCO colleagues Peter Fox and Ahmed Eleish at the Keck Foundation sponsored Deep-Time Data Infrastructure Data Science Teams at Rensselaer Polytechnic Institute, Troy NY.

“The quest for new mineral deposits is incessant, but until recently mineral discovery has been more a matter of luck than scientific prediction,” says Dr. Morrison. “All that may change thanks to big data.”

Humans have collected a vast amount of information on Earth’s more than 5,200 known mineral species (each of which has a unique combination of chemical composition and atomic structure).

Millions of mineral specimens from hundreds of thousands of localities around the world have been described and catalogued. Databases containing details of where each mineral was discovered, all of its known occurrences, and the ages of those deposits are large and growing by the week.

Databases also record essential information on chemical compositions and a host of physical properties, including hardness, color, atomic structure, and more.

Coupled with data on the surrounding geography, the geological setting, and coexisting minerals, Earth scientists now have access to “big data” resources ripe for analysis.

Until recently, scientists didn’t have the necessary modelling and visualization tools to capitalize on these giant stockpiles of information.

Network analysis offers new insight into minerals, just as complex data sets offer important understanding of social media connections, city traffic patterns, and metabolic pathways, to name a few examples.

“Big data is a big thing,” says Dr. Hazen. “You hear about it in all kinds of fields—medicine, commerce; even the US National Security Agency uses it to analyze phone records—but until recently no one had applied big data methods to mineralogy and petrology.”

The network analysis technique enables Earth scientists to represent data from multiple variables on thousands of minerals sampled from hundreds of thousands of locations within a single graph.

These visualizations can reveal patterns of occurrence and distribution that might otherwise be hidden within a spreadsheet.

In other words, big data provides an intimate picture of which minerals coexist with each other, as well as what geological, physical, chemical, and (perhaps most surprising) biological characteristics are necessary for their appearance.

From those insights it’s a relatively simple step to predict what minerals are missing from scientific lists, as well as where to go to find new deposits.

Says Dr. Hazen: “Network analysis can provide visual clues to mineralogists regarding where to go and what to look for. This is a brand new idea in the paper and I think it will open up an entirely new direction in mineralogy.”

Already the technique has been used to predict 145 missing carbon-bearing minerals and where to find them, leading to creation of the Deep Carbon Observatory’s Carbon Mineral Challenge. Ten have been found so far.

The estimate came from a statistical analysis of carbon-bearing minerals known today, then extrapolating how many scientists should be looking for.

Predicted before they were found

“We have used the same kinds of techniques to predict that at least 1,500 minerals of all kinds are ‘missing,’ to predict what some of them are, and where to find them,” Dr. Hazen says.

Says Dr. Morrison: “These new approaches to data-driven discovery allow us to predict both minerals unknown to science today and the location of new deposits.

Additionally, understanding how minerals have changed through geologic time, coupled with our knowledge of biology, is leading to new insights regarding the co-evolution of the geosphere and biosphere. ”

In a test case, the researchers explored minerals containing copper, which plays critical roles in modern society (e.g., pipes, wires), as well as essential roles in biological evolution. The element is extremely sensitive to oxygen, so the nature of copper in a mineral offers a clue to the level of oxygen in the atmosphere at the time the mineral formed.

The investigators also performed an analysis of common minerals in igneous rocks-those formed from a hot molten state. The mineral networks of igneous rocks revealed through big data recreated “Bowen’s reaction series” (based on Norman L. Bowen’s painstaking lab experiments in the early 1900s), which shows how a sequence of characteristic minerals appears as the magma cools.

The analysis showed the exact same sequence of minerals embedded in the mineral networks.

The researchers hope that these techniques will lead to an understanding and appreciation of previously unrecognized mineral relationships in varied mineral deposits.

Mineral networks will also serve as effective visual tools for learning about mineralogy and petrology – the branches of science concerned with the origin, composition, structure, properties, and classification of rocks and minerals.

Network analysis has numerous potential applications in geology, both for research and mineral exploration.

Mining companies could use the technology to predict the locations of unknown mineral deposits based on existing data.

Researchers could use these tools to explain how Earth’s minerals have changed over time and incorporate data from biomarker molecules to show how cells and minerals interact.

And ore geologists hope to use mineral network analysis to lead to valuable new deposits.

Dr. Morrison also hopes to use network analysis to reveal the geologic history of other planets. She is a member of the NASA Mars Curiosity Rover team identifying Martian minerals through X-ray diffraction data sent back to Earth. By applying these tools to analyze sedimentary environments on Earth, she believes scientists may also start answering similar questions about Mars.

“Minerals provide the basis for all our material wealth,” she notes, “not just precious gold and brilliant gemstones, but in the brick and steel of every home and office, in cars and planes, in bottles and cans, and in every high-tech gadget from laptops to iPhones.”

“Minerals form the soils in which we grow our crops, they provide the gravel with which we pave our roads, and they filter the water we drink.”

“This new tool for understanding minerals represents an important advance in a scientific field of vital interest.”

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